





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCAmelCase		:  List[str]         =	logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase		:  Tuple         =	'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
'''







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a   ,		a=8    ) -> Union[str, Any]:
					__A      :		str         =							height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__A      :		str         =							width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a=5_12   ,		a=5_12    ) -> Optional[Any]:
					__A      :		str         =							pil_image.resize((w, h)   ,		resample=Image.BICUBIC   ,		reducing_gap=1    )
					__A      :		str         =							np.array(pil_image.convert('RGB'    )    )
					__A      :		List[Any]         =							arr.astype(np.floataa    ) / 1_27.5 - 1
					__A      :		Any         =							np.transpose(a_   ,		[2, 0, 1]    )
					__A      :		Optional[int]         =							torch.from_numpy(a_    ).unsqueeze(0    )
					return image





class 		_A(   _A  ):


				"""simple docstring"""


				def __init__(     self	,   _A	,   _A	,   _A	,   ):
									super().__init__()

									self.register_modules(
									    unet=a_	,   scheduler=a_	,   movq=a_	,   )
									__A      :		Any         =							2 ** (len(self.movq.config.block_out_channels	) - 1)



				def    UpperCAmelCase_						(     self	,   _A	,   _A	,   _A	):
									# get the original timestep using init_timestep
									__A      :		Optional[Any]         =							min(int(num_inference_steps * strength	)	,   a_	)

									__A      :		str         =							max(num_inference_steps - init_timestep	,   0	)
									__A      :		List[Any]         =							self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start



				def    UpperCAmelCase_						(     self	,   _A	,   _A	,   _A	,   _A	,   _A	,   _A	,   _A=None	):
									if not isinstance(a_	,   (torch.Tensor, PIL.Image.Image, list)	):
														raise ValueError(
														    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a_	)}"""	)

									__A      :		int         =							image.to(device=a_	,   dtype=a_	)

									__A      :		int         =							batch_size * num_images_per_prompt

									if image.shape[1] == 4:
														__A      :		int         =							image

									else:
														if isinstance(a_	,   a_	) and len(a_	) != batch_size:
																			raise ValueError(
																			    F"""You have passed a list of generators of length {len(a_	)}, but requested an effective batch"""
																			    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""	)

														elif isinstance(a_	,   a_	):
																			__A      :		Union[str, Any]         =							[
																			    self.movq.encode(image[i : i + 1]	).latent_dist.sample(generator[i]	) for i in range(a_	)
																			]
																			__A      :		Union[str, Any]         =							torch.cat(a_	,   dim=0	)
														else:
																			__A      :		Any         =							self.movq.encode(a_	).latent_dist.sample(a_	)

														__A      :		Tuple         =							self.movq.config.scaling_factor * init_latents

									__A      :		List[str]         =							torch.cat([init_latents]	,   dim=0	)

									__A      :		Dict         =							init_latents.shape
									__A      :		int         =							randn_tensor(a_	,   generator=a_	,   device=a_	,   dtype=a_	)

									# get latents
									__A      :		Any         =							self.scheduler.add_noise(a_	,   a_	,   a_	)

									__A      :		Optional[Any]         =							init_latents

									return latents



				def    UpperCAmelCase_						(     self	,   _A=0	):
									if is_accelerate_available():
														from accelerate import cpu_offload
									else:
														raise ImportError('Please install accelerate via `pip install accelerate`'	)

									__A      :		Tuple         =							torch.device(F"""cuda:{gpu_id}"""	)

									__A      :		Tuple         =							[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																			cpu_offload(a_	,   a_	)



				def    UpperCAmelCase_						(     self	,   _A=0	):
									if is_accelerate_available() and is_accelerate_version('>='	,   '0.17.0.dev0'	):
														from accelerate import cpu_offload_with_hook
									else:
														raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'	)

									__A      :		Optional[int]         =							torch.device(F"""cuda:{gpu_id}"""	)

									if self.device.type != "cpu":
														self.to('cpu'	,   silence_dtype_warnings=a_	)
														torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									__A      :		Optional[Any]         =							None
									for cpu_offloaded_model in [self.unet, self.movq]:
														__A      :		int         =							cpu_offload_with_hook(a_	,   a_	,   prev_module_hook=a_	)

									# We'll offload the last model manually.
									__A      :		Dict         =							hook



				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def    UpperCAmelCase_						(     self	):
									if not hasattr(self.unet	,   '_hf_hook'	):
														return self.device
									for module in self.unet.modules():
														if (
														    hasattr(a_	,   '_hf_hook'	)
														    and hasattr(module._hf_hook	,   'execution_device'	)
														    and module._hf_hook.execution_device is not None
														):
																			return torch.device(module._hf_hook.execution_device	)
									return self.device





				@torch.no_grad()
				@replace_example_docstring(a_	)
				def __call__(     self	,   _A	,   _A	,   _A	,   _A = 512	,   _A = 512	,   _A = 100	,   _A = 4.0	,   _A = 0.3	,   _A = 1	,   _A = None	,   _A = "pil"	,   _A = True	,   ):
									__A      :		Optional[Any]         =							self._execution_device

									__A      :		Union[str, Any]         =							guidance_scale > 1.0

									if isinstance(a_	,   a_	):
														__A      :		Any         =							torch.cat(a_	,   dim=0	)
									__A      :		List[str]         =							image_embeds.shape[0]
									if isinstance(a_	,   a_	):
														__A      :		Optional[Any]         =							torch.cat(a_	,   dim=0	)

									if do_classifier_free_guidance:
														__A      :		Dict         =							image_embeds.repeat_interleave(a_	,   dim=0	)
														__A      :		Any         =							negative_image_embeds.repeat_interleave(a_	,   dim=0	)

									__A      :		Any         =							torch.cat([negative_image_embeds, image_embeds]	,   dim=0	).to(dtype=self.unet.dtype	,   device=a_	)

									if not isinstance(a_	,   a_	):
														__A      :		Tuple         =							[image]
									if not all(isinstance(a_	,   (PIL.Image.Image, torch.Tensor)	) for i in image	):
														raise ValueError(
														    F"""Input is in incorrect format: {[type(a_	) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""	)

									__A      :		List[Any]         =							torch.cat([prepare_image(a_	,   a_	,   a_	) for i in image]	,   dim=0	)
									__A      :		Union[str, Any]         =							image.to(dtype=image_embeds.dtype	,   device=a_	)

									__A      :		int         =							self.movq.encode(a_	)["latents"]
									__A      :		Tuple         =							latents.repeat_interleave(a_	,   dim=0	)
									self.scheduler.set_timesteps(a_	,   device=a_	)
									__A      :		Dict         =							self.get_timesteps(a_	,   a_	,   a_	)
									__A      :		Any         =							timesteps[:1].repeat(batch_size * num_images_per_prompt	)
									__A      :		List[Any]         =							downscale_height_and_width(a_	,   a_	,   self.movq_scale_factor	)
									__A      :		Optional[int]         =							self.prepare_latents(
									    a_	,   a_	,   a_	,   a_	,   image_embeds.dtype	,   a_	,   a_	)
									for i, t in enumerate(self.progress_bar(a_	)	):
														# expand the latents if we are doing classifier free guidance
														__A      :		List[Any]         =							torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

														__A      :		List[Any]         =							{"image_embeds": image_embeds}
														__A      :		List[str]         =							self.unet(
														    sample=a_	,   timestep=a_	,   encoder_hidden_states=a_	,   added_cond_kwargs=a_	,   return_dict=a_	,   )[0]

														if do_classifier_free_guidance:
																			__A      :		Optional[Any]         =							noise_pred.split(latents.shape[1]	,   dim=1	)
																			__A      :		List[Any]         =							noise_pred.chunk(2	)
																			__A      :		Optional[Any]         =							variance_pred.chunk(2	)
																			__A      :		Optional[Any]         =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			__A      :		Optional[Any]         =							torch.cat([noise_pred, variance_pred_text]	,   dim=1	)

														if not (
														    hasattr(self.scheduler.config	,   'variance_type'	)
														    and self.scheduler.config.variance_type in ["learned", "learned_range"]
														):
																			__A      :		Any         =							noise_pred.split(latents.shape[1]	,   dim=1	)

														# compute the previous noisy sample x_t -> x_t-1
														__A      :		Tuple         =							self.scheduler.step(
														    a_	,   a_	,   a_	,   generator=a_	,   )[0]

									# post-processing
									__A      :		Any         =							self.movq.decode(a_	,   force_not_quantize=a_	)["sample"]

									if output_type not in ["pt", "np", "pil"]:
														raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""	)

									if output_type in ["np", "pil"]:
														__A      :		Any         =							image * 0.5 + 0.5
														__A      :		Any         =							image.clamp(0	,   1	)
														__A      :		Tuple         =							image.cpu().permute(0	,   2	,   3	,   1	).float().numpy()

									if output_type == "pil":
														__A      :		List[Any]         =							self.numpy_to_pil(a_	)

									if not return_dict:
														return (image,)

									return ImagePipelineOutput(images=a_	)











'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



